Machine learning solutions applied to amyotrophic lateral sclerosis prognosis: a review
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/handle/123456789/54162 https://doi.org/10.3389/fcomp.2022.869140 |
Resumo: | The prognosis of Amyotrophic Lateral Sclerosis (ALS), a complex and rare disease, represents a challenging and essential task to better comprehend its progression and improve patients' quality of life. The use of Machine Learning (ML) techniques in healthcare has produced valuable contributions to the prognosis field. This article presents a systematic and critical review of primary studies that used ML applied to the ALS prognosis, searching for databases, relevant predictor biomarkers, the ML algorithms and techniques, and their outcomes. We focused on studies that analyzed biomarkers commonly present in the ALS disease clinical practice, such as demographic, clinical, laboratory, and imaging data. Hence, we investigate studies to provide an overview of solutions that can be applied to develop decision support systems and be used by a higher number of ALS clinical settings. The studies were retrieved from PubMed, Science Direct, IEEEXplore, and Web of Science databases. After completing the searching and screening process, 10 articles were selected to be analyzed and summarized. The studies evaluated and used different ML algorithms, techniques, datasets, sample sizes, biomarkers, and performance metrics. Based on the results, three distinct types of prediction were identified: Disease Progression, Survival Time, and Need for Support. The biomarkers identified as relevant in more than one study were the ALSFRS/ALSFRS-R, disease duration, Forced Vital Capacity, Body Mass Index, age at onset, and Creatinine. In general, the studies presented promissory results that can be applied in developing decision support systems. Besides, we discussed the open challenges, the limitations identified, and future research opportunities. |
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Dourado Junior, Mário Emílio TeixeiraPapaiz, FabianoValentim, Ricardo Alexsandro de MedeirosMorais, Antonio Higor Freire deArrais, Joel Perdizhttps://orcid.org/0000-0002-9462-22942023-07-25T19:30:23Z2023-07-25T19:30:23Z2022PAPAIZ, Fabiano; DOURADO, Mario Emílio Teixeira; VALENTIM, Ricardo Alexsandro de Medeiros; MORAIS, Antonio Higor Freire de; ARRAIS, Joel Perdiz. Machine Learning Solutions Applied to Amyotrophic Lateral Sclerosis Prognosis: a review. Frontiers In Computer Science, [S.L.], v. 4, p. 1, 28 abr. 2022. Frontiers Media SA. http://dx.doi.org/10.3389/fcomp.2022.869140. Disponível em: https://www.frontiersin.org/articles/10.3389/fcomp.2022.869140/full. Acesso em: 13 jul. 2023.https://repositorio.ufrn.br/handle/123456789/54162https://doi.org/10.3389/fcomp.2022.869140Computer ScienceAttribution 3.0 Brazilhttp://creativecommons.org/licenses/by/3.0/br/info:eu-repo/semantics/openAccessamyotrophic lateral sclerosisprognosismachine learninghealth informaticsliterature reviewMachine learning solutions applied to amyotrophic lateral sclerosis prognosis: a reviewinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleThe prognosis of Amyotrophic Lateral Sclerosis (ALS), a complex and rare disease, represents a challenging and essential task to better comprehend its progression and improve patients' quality of life. The use of Machine Learning (ML) techniques in healthcare has produced valuable contributions to the prognosis field. This article presents a systematic and critical review of primary studies that used ML applied to the ALS prognosis, searching for databases, relevant predictor biomarkers, the ML algorithms and techniques, and their outcomes. We focused on studies that analyzed biomarkers commonly present in the ALS disease clinical practice, such as demographic, clinical, laboratory, and imaging data. Hence, we investigate studies to provide an overview of solutions that can be applied to develop decision support systems and be used by a higher number of ALS clinical settings. The studies were retrieved from PubMed, Science Direct, IEEEXplore, and Web of Science databases. After completing the searching and screening process, 10 articles were selected to be analyzed and summarized. The studies evaluated and used different ML algorithms, techniques, datasets, sample sizes, biomarkers, and performance metrics. Based on the results, three distinct types of prediction were identified: Disease Progression, Survival Time, and Need for Support. The biomarkers identified as relevant in more than one study were the ALSFRS/ALSFRS-R, disease duration, Forced Vital Capacity, Body Mass Index, age at onset, and Creatinine. In general, the studies presented promissory results that can be applied in developing decision support systems. Besides, we discussed the open challenges, the limitations identified, and future research opportunities.engreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNORIGINALMachineLearningSolutions_DouradoJunior_2022.pdfMachineLearningSolutions_DouradoJunior_2022.pdfapplication/pdf595988https://repositorio.ufrn.br/bitstream/123456789/54162/1/MachineLearningSolutions_DouradoJunior_2022.pdf2eb06d37a4a3e4433d77e29a5430f115MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81484https://repositorio.ufrn.br/bitstream/123456789/54162/2/license.txte9597aa2854d128fd968be5edc8a28d9MD52123456789/541622023-07-25 16:31:33.852oai:https://repositorio.ufrn.br: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Repositório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2023-07-25T19:31:33Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pt_BR.fl_str_mv |
Machine learning solutions applied to amyotrophic lateral sclerosis prognosis: a review |
title |
Machine learning solutions applied to amyotrophic lateral sclerosis prognosis: a review |
spellingShingle |
Machine learning solutions applied to amyotrophic lateral sclerosis prognosis: a review Dourado Junior, Mário Emílio Teixeira amyotrophic lateral sclerosis prognosis machine learning health informatics literature review |
title_short |
Machine learning solutions applied to amyotrophic lateral sclerosis prognosis: a review |
title_full |
Machine learning solutions applied to amyotrophic lateral sclerosis prognosis: a review |
title_fullStr |
Machine learning solutions applied to amyotrophic lateral sclerosis prognosis: a review |
title_full_unstemmed |
Machine learning solutions applied to amyotrophic lateral sclerosis prognosis: a review |
title_sort |
Machine learning solutions applied to amyotrophic lateral sclerosis prognosis: a review |
author |
Dourado Junior, Mário Emílio Teixeira |
author_facet |
Dourado Junior, Mário Emílio Teixeira Papaiz, Fabiano Valentim, Ricardo Alexsandro de Medeiros Morais, Antonio Higor Freire de Arrais, Joel Perdiz |
author_role |
author |
author2 |
Papaiz, Fabiano Valentim, Ricardo Alexsandro de Medeiros Morais, Antonio Higor Freire de Arrais, Joel Perdiz |
author2_role |
author author author author |
dc.contributor.authorID.pt_BR.fl_str_mv |
https://orcid.org/0000-0002-9462-2294 |
dc.contributor.author.fl_str_mv |
Dourado Junior, Mário Emílio Teixeira Papaiz, Fabiano Valentim, Ricardo Alexsandro de Medeiros Morais, Antonio Higor Freire de Arrais, Joel Perdiz |
dc.subject.por.fl_str_mv |
amyotrophic lateral sclerosis prognosis machine learning health informatics literature review |
topic |
amyotrophic lateral sclerosis prognosis machine learning health informatics literature review |
description |
The prognosis of Amyotrophic Lateral Sclerosis (ALS), a complex and rare disease, represents a challenging and essential task to better comprehend its progression and improve patients' quality of life. The use of Machine Learning (ML) techniques in healthcare has produced valuable contributions to the prognosis field. This article presents a systematic and critical review of primary studies that used ML applied to the ALS prognosis, searching for databases, relevant predictor biomarkers, the ML algorithms and techniques, and their outcomes. We focused on studies that analyzed biomarkers commonly present in the ALS disease clinical practice, such as demographic, clinical, laboratory, and imaging data. Hence, we investigate studies to provide an overview of solutions that can be applied to develop decision support systems and be used by a higher number of ALS clinical settings. The studies were retrieved from PubMed, Science Direct, IEEEXplore, and Web of Science databases. After completing the searching and screening process, 10 articles were selected to be analyzed and summarized. The studies evaluated and used different ML algorithms, techniques, datasets, sample sizes, biomarkers, and performance metrics. Based on the results, three distinct types of prediction were identified: Disease Progression, Survival Time, and Need for Support. The biomarkers identified as relevant in more than one study were the ALSFRS/ALSFRS-R, disease duration, Forced Vital Capacity, Body Mass Index, age at onset, and Creatinine. In general, the studies presented promissory results that can be applied in developing decision support systems. Besides, we discussed the open challenges, the limitations identified, and future research opportunities. |
publishDate |
2022 |
dc.date.issued.fl_str_mv |
2022 |
dc.date.accessioned.fl_str_mv |
2023-07-25T19:30:23Z |
dc.date.available.fl_str_mv |
2023-07-25T19:30:23Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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publishedVersion |
dc.identifier.citation.fl_str_mv |
PAPAIZ, Fabiano; DOURADO, Mario Emílio Teixeira; VALENTIM, Ricardo Alexsandro de Medeiros; MORAIS, Antonio Higor Freire de; ARRAIS, Joel Perdiz. Machine Learning Solutions Applied to Amyotrophic Lateral Sclerosis Prognosis: a review. Frontiers In Computer Science, [S.L.], v. 4, p. 1, 28 abr. 2022. Frontiers Media SA. http://dx.doi.org/10.3389/fcomp.2022.869140. Disponível em: https://www.frontiersin.org/articles/10.3389/fcomp.2022.869140/full. Acesso em: 13 jul. 2023. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/handle/123456789/54162 |
dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.3389/fcomp.2022.869140 |
identifier_str_mv |
PAPAIZ, Fabiano; DOURADO, Mario Emílio Teixeira; VALENTIM, Ricardo Alexsandro de Medeiros; MORAIS, Antonio Higor Freire de; ARRAIS, Joel Perdiz. Machine Learning Solutions Applied to Amyotrophic Lateral Sclerosis Prognosis: a review. Frontiers In Computer Science, [S.L.], v. 4, p. 1, 28 abr. 2022. Frontiers Media SA. http://dx.doi.org/10.3389/fcomp.2022.869140. Disponível em: https://www.frontiersin.org/articles/10.3389/fcomp.2022.869140/full. Acesso em: 13 jul. 2023. |
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https://repositorio.ufrn.br/handle/123456789/54162 https://doi.org/10.3389/fcomp.2022.869140 |
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